Since the perturbations happen on the entire image or on randomly selected squares, respectively, they evaluate the overall attribution map. Infidelity scales linearly with the number of samples \(k\)  used to approximate the expected value. In this work, \(k = 1000\) , which corresponds to the original implementation by [1]}.
